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Chin. Phys. B, 2021, Vol. 30(9): 090501    DOI: 10.1088/1674-1056/abe92f
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Identification of unstable individuals in dynamic networks

Dongli Duan(段东立)1,†, Tao Chai(柴涛)1, Xixi Wu(武茜茜)1, Chengxing Wu(吴成星)1, Shubin Si(司书宾)2,3, and Genqing Bian(边根庆)1
1 School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710311, China;
2 School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China;
3 Key Laboratory of Industrial Engineering and Intelligent Manufacturing(Ministry of Industry and Information Technology), Northwestern Polytechnical University, Xi'an 710072, China
Abstract  To identify the unstable individuals of networks is of great importance for information mining and security management. Exploring a broad range of steady-state dynamical processes including biochemical dynamics, epidemic processes, birth-death processes and regulatory dynamics, we propose a new index from the microscopic perspective to measure the stability of network nodes based on the local correlation matrix. The proposed index describes the stability of each node based on the activity change of the node after its neighbor is disturbed. Simulation and comparison results show our index can identify the most unstable nodes in the network with various dynamical behaviors, which would actually create a richer way and a novel insight of exploring the problem of network controlling and optimization.
Keywords:  network      dynamic behaviors      stability      perturbation  
Received:  23 December 2020      Revised:  03 February 2021      Accepted manuscript online:  24 February 2021
PACS:  05.10.-a (Computational methods in statistical physics and nonlinear dynamics)  
  64.60.aq (Networks)  
  89.75.-k (Complex systems)  
  89.75.Hc (Networks and genealogical trees)  
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 72071153 and 71771186), Key Laboratory of Science and Technology on Integrated Logistics Support (Grant Nos. 6142003190102), the Natural Science Foundation of Shaanxi Province, China (Grant Nos. 2020JM-486), and the China Postdoctoral Science Foundation (Grant No. 2017M613336).
Corresponding Authors:  Dongli Duan     E-mail:  mineduan@163.com

Cite this article: 

Dongli Duan(段东立), Tao Chai(柴涛), Xixi Wu(武茜茜), Chengxing Wu(吴成星), Shubin Si(司书宾), and Genqing Bian(边根庆) Identification of unstable individuals in dynamic networks 2021 Chin. Phys. B 30 090501

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